Integrated digital thread for additive manufacturing design optimization of lightweight structures

ABSTRACT

A part formed by an additive manufacturing process is provided, which consists of three (3) regions: regions of voids with no material; regions of solid material; and regions of non-uniform lattice cells. The regions are spatially distributed throughout the part as a function of load conditions such that the solid material is distributed in regions of first load paths and the lattice cells are distributed in regions of second load paths lower in magnitude than the first load paths. The lattice cells are tailored to the additive manufacturing process constraints and machine resolution.

FIELD

The present disclosure relates to additive manufacturing and moreparticularly to a method of designing a part to be formed by an additivemanufacturing process.

BACKGROUND

The statements in this section merely provide background informationrelated to the present disclosure and may not constitute prior art.

Additive manufacturing (AM) or 3D printing has been used to buildcomplex objects with a wide variety of materials and functions. AMprovides great opportunities for the manufacturing of innovativestructure designs of complex geometrical features. However, suchstructures cannot be obtained by the traditional design methods, such asparametric structure optimization, due to its limited capability indesigning 3D structures with irregular, multiscale geometrical features.

To resolve this issue, Topology optimization (TO) has been used.Topology optimization (TO) is a mathematical method that optimizesmaterial layout within a given design space, for a given set of loads,boundary conditions and constraints with the goal of maximizing theperformance of the system. TO can facilitate weight reduction bydistributing the materials to the optimum locations.

The existing TO tools for AM design encounter some issues. First, thetraditional TO methods (e.g. the gradient-based methods) always generate“grey” elements in the final design. Ideally, there should be only twotypes of elements in the final design: “white” elements (0) thatrepresent the voids, and “black” elements (1) that represent the solidmaterial. However, exiting software tools generate the “grey” elementsof values between 0 and 1. In engineering practice, designers alwaysmanually set a threshold to classify the grey elements into either 0or 1. Such ad-hoc binarization step diverges the result from the globaloptimization.

Second, while the existing software tools offer the capability ofreplacing the “grey” elements by lattice cells, no tool has been used todetermine an appropriate lattice properties based on the manufacturingcapability of the AM equipment and method.

Third, the TO tools cannot be used to create geometrical features thatare required by AM constraints (e.g. egress holes).

Fourth, no process is known to integrate CAD (Computer Aided Design),CAE (Computer Aided Engineering), multiscale TO and AM. The existingsoftware tools can focus on only one or two aspects of the process.

These issues associated with the topology of parts manufactured using avariety of AM techniques is addressed by the present disclosure.

SUMMARY

In one form, a part formed by an additive manufacturing process isprovided, which consists of three (3) regions: regions of voids with nomaterial; regions of solid material; and regions of non-uniform latticecells. The regions are spatially distributed throughout the part as afunction of load conditions such that the solid material is distributedin regions of first load paths and the lattice cells are distributed inregions of second load paths lower in magnitude than the second loadpaths.

In other features, the lattice cells comprise 6-bar tetrahedral latticecells, 16-bar hexahedral elements, and 24-bar hexahedral elements. Thetype and size of the lattice cells are a function of the additivemanufacturing process. The diameters of each bar of the lattice cellsare not equal and the diameters of each bar of the lattice are afunction of at least one resolution unit of the additive manufacturingprocess and part performance requirements. The material of the part maybe selected from the group consisting of metals, ceramics, polymers,composites and plastics.

In another form, a process of designing a part formed by an additivemanufacturing process is provided, which includes generating a 3D meshtopology. The 3D mesh topology consists of three (3) regions: regions ofvoids with no material; regions of solid material; and regions ofnon-uniform lattice cells. The regions are spatially distributedthroughout the part as a function of load conditions such that the solidmaterial is distributed in regions of first load paths (also referred toas “primary” load paths) and the lattice cells are distributed inregions of second load paths lower in magnitude than the first loadpaths.

In other features, the diameters of each bar of the lattice cells areoptimized based on the load conditions such that the diameters are notequal. The diameters of each bar of the lattice are adjusted as afunction of at least one of a resolution unit of the additivemanufacturing process and part performance requirements. The processfurther includes developing a statistical analysis model to generate astatistical distribution of bar diameters and classifying bars of eachlattice into clusters, wherein all lattice cells in a same cluster areassigned an average diameter of the cluster. The average diameter of thecluster is adjusted as a function of at least one of a resolution unitof the additive manufacturing process and part performance requirements.The process further comprises a validation step to verify that designrequirements are met by the 3D mesh topology and a step of smoothing,surfacing, and fixing the 3D mesh topology to meet A-surfacerequirements. The process further includes generating additionalstructural elements, such as geometrical features, as a function of theadditive manufacturing process. The geometrical features include egressslots for un-sintered powder of a selective laser sintering (SLS)process or for un-cured resin of a stereolithography (SLA) process.

In still another form, a method of manufacturing a part using anadditive manufacturing process is provided, which includes:manufacturing regions of solid material; manufacturing regions ofnon-uniform lattice cells; and leaving regions of voids with nomaterial. The regions are spatially distributed throughout the part as afunction of load conditions such that the solid material is distributedin regions of first load paths (also referred to as “primary” loadpaths) and the lattice cells are distributed in regions of second loadpaths lower in magnitude than the first load paths.

In other features, the diameters of each bar of the non-uniform latticecells are adjusted as a function of at least one of a resolution unit ofthe additive manufacturing process and part performance requirements andthe part may be manufactured from different additive manufacturingprocesses, such as selective laser sintering (SLD), stereolithography(SLA), fused deposition modeling (FDM).

Further areas of applicability will become apparent from the descriptionprovided herein. It should be understood that the description andspecific examples are intended for purposes of illustration only and arenot intended to limit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description and the accompanying drawings, wherein:

FIG. 1 depicts a flow diagram of a method of designing a part to beformed by an additive manufacturing process in accordance with theteachings of the present disclosure;

FIG. 2 depicts a divided bar chart illustrating the thresholds toclassify elements into voids, lattice cells, and solid materials;

FIGS. 3A to 3C depict three types of lattice cells used in the topologyoptimization, wherein FIG. 3A is a 6-bar tetrahedral lattice cell, FIG.3B is a 16-bar hexahedral lattice cell, and FIG. 3C is a 24-barhexahedral lattice cell;

FIG. 4 is a bar diagram showing a statistical distribution of bardiameters;

FIG. 5 depicts a design image after lattice dimension optimization;

FIG. 6A depicts a 3D design image after topology optimization andlattice dimension optimization;

FIG. 6B is an enlarged view of portion A of FIG. 6A;

FIG. 7A is another 3D design image after topology optimization andlattice dimension optimization; and

FIG. 7B depicts a lattice cell in the 3D design of FIG. 7A.

Corresponding reference numerals indicate corresponding parts throughoutthe several views of the drawings.

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is notintended to limit the present disclosure, application, or uses.

The present disclosure provides a process of designing a part to beformed by an additive manufacturing (AM) process. The process of thepresent disclosure provides an integrated digital thread for successfulimplementation of the design for an AM process. The digital thread asused herein refers to the communication framework that allows aconnected data flow and integrated view of the data throughout aproduct's design cycle, which includes computer aided design (CAD),computer aided engineering (CAE), topology optimization (TO), takinginto consideration manufacturing constraints of a particular AM process.The digital thread can improve product quality by avoiding mistakes inmanual translations of engineering specifications along the productvalue chain, improve velocity of new product introductions andcommunication of engineering changes along the product value chain,increase efficiency of digitally capturing and analyzing data related toproduct manufacturing, and allow manufacturers to deliver new servicesto customers along with physical product leveraging the digital data nowavailable on the product.

Referring to FIG. 1, a method 10 of designing a part to be formed by andsuitable for an additive manufacturing process in accordance with theteachings of the present disclosure starts with creating a CAD model forthe part in step 12. The part may be an auto part or any structure whereweight reduction is desirable. The CAD model is converted into a CAEmodel in the form of a 3D mesh in the CAE environment in step 14. Thematerial properties of the part are assigned to the CAE model in step16. The material of the part may be metals, ceramics, polymers,composites and plastics. The design requirements are generated in step18 and are translated into the loading condition, boundary condition,optimization objective and design constraints in step 20. Step 12through step 20 constitute a CAD-CAE integration process. In the CAD-CAEintegration process, a CAE model with structural optimization setups isgenerated.

Next, the element density thresholds for voids, solid materials, andlattice cells are established in step 22. The baseline design in the CAEmodel undergoes topology optimization in step 24, where the structure ofthe baseline design is optimized based on the element density thresholdsto achieve the maximum weight reduction and to meet the designrequirements on the performances (e.g. stiffness). Step 22 and step 24constitute a multiscale topology optimization process.

Topology optimization (TO) is a mathematical method that optimizesmaterial layout within a given design space, for a given set of loads,boundary conditions and constraints with the goal of maximizing theperformance of the system. TO can facilitate weight reduction bydistributing the materials to the optimum locations. The multiscale TOprocess is employed to distribute solid materials, voids, and latticecells to the optimal locations.

Ideally, there should be only two types of elements in the final designso that a 3D printing machine can process and manufacture: “white”elements (0) that represent the voids, and “black” elements (1) thatrepresent the solid material. However, exiting software tools generatethe “grey” elements of values between 0 and 1. Traditionally, designersmanually set a threshold to classify the grey elements into either 0or 1. Such ad-hoc binarization step, however, diverges the result fromthe global optimization.

In the multiscale TO process of the present disclosure, the greyelements are not simply classified into either 0 or 1. Instead, latticecells may be used for the grey elements. Therefore, in the multiscale TOprocess, a 3D mesh topology is generated, which consists of threeregions: regions of voids with no material, regions of solid material,and regions of non-uniform lattice cells. These regions are spatiallydistributed throughout the part as a function of load conditions suchthat the solid material is distributed in regions of relatively higherloads, load paths, or stress and the lattice cells are distributed inregions of relatively lower loads, load paths or stress lower than theregions of solid material.

Referring to FIG. 2, two element density thresholds may be used toclassify the “grey elements” into solid materials, voids, or latticecells. An element with density equal to or above 0.9 is classified as“solid”. An element with density below 0.05 is classified as “void”. Anelement with density between 0.05 and 0.9 is classified as “latticecell.”

Referring to FIGS. 3A, 3B and 3C, three types of lattice cells may beused depending on the shape and density of the elements: 6-bartetrahedral lattice cells 50 for the tetrahedral elements, 16-barhexahedral lattice cells 52 or 24-bar hexahedral lattice cells 54 forthe hexahedral elements on the part being designed. These lattice cellshave different number and diameter of bars 56 and shape. Therefore, thetype and size of lattice cells 50, 52, 54 being used depends on theshape of the elements on the part being designed and are also a functionof the additive manufacturing process. The diameter of the bars 56 inthe same lattice cell may be the same or different and may be determinedby the optimization algorithm. A lattice cell having more bars and barsof larger diameters may be assigned to an element with higher density. Alattice having few bars and bars of smaller diameters may be assigned toan element with lower density. For example, a 24-bar hexahedral latticecell may be chosen for an element with higher density and a 6-bartetrahedral lattice may be chosen for an element with lower density. Inthis multiscale TO process, the diameters of the bars 56 in the latticecells 50, 52, 54 are chosen/designed without considering the resolution(i.e., the voxel) of the AM machine.

Referring to back to FIG. 1, after the diameters of the bars 56 of thelattice cells 50, 52, 54 are decided and chosen, a statistical analysismodel is developed, a statistical distribution of bar diameters isgenerated, and the diameters of the bars in the lattice cells arestatistically analyzed in step 26. The lattice dimension is thenadjusted and optimized based on the statistical analysis in step 28. Anew design with the adjusted and optimized lattice dimensions isvalidated in step 30. Step 26 through step 30 constitute a statisticalanalysis and lattice dimension optimization process, which optimizes thediameter of the bars of the lattice cells based on a pixel length (or avoxel) of the 3D printing machine.

In the lattice design, the typical TO software cannot take intoconsideration of the manufacturing constraints (e.g. resolution of theAM machine) in the topology optimization process. Therefore, thediameters of the bars 56 in the lattice cells 50, 52, 54 are designedand decided without considering the resolution of the AM machine and maybe a value that cannot be printed by a 3D printing machine having apredetermined pixel length.

The statistical analysis and lattice dimension optimization process ofthe present disclosure is a post-processing tool, which generates thefinal lattice designs based on the statistical information of the raw TOresults. In the statistical analysis process, the diameter of the bars56 is rounded into an integer that is multiple of a pixel length of a 3Dprinting machine to make the final design suitable for 3D printing.Therefore, the final lattice structure design obtained after thestatistical analysis and lattice dimension optimization process willsatisfy the manufacturing constraints of a particular additivemanufacturing process to be used, while maintaining the reduced weightand the superior performances of the TO results. The additivemanufacturing process may be selected from the group consisting ofselective laser sintering (SLS), stereolithography (SLA), fuseddeposition modeling (FDM), polyjet (PJ), direct metal laser sintering(DMLS), selective layer melting (SLM), continuous liquid interphaseproduction (CLIP), laminated object manufacturing (LOM).

Referring to FIG. 4, a statistical analysis model is developed togenerate a statistical distribution of bar diameters. Based on thestatistical distribution of the bar diameters, the lattice bars of eachlattice are classified into several clusters. An average diameter isassigned to all lattice cells in the same cluster. The diameters of thetruss elements in the same cluster are very close to each other, andtruss elements from different clusters can be easily distinguished bythe difference in the bar diameter values. All lattice cells in the samecluster are assigned with the average diameter of the cluster (roundedto the closest integral number that is multiple of a pixel length). Inother words, the average diameter of the cluster is adjusted as afunction of a resolution unit of the additive manufacturing machine,being an integer equal to or multiple of a pixel length of a 3D printingmachine, as well as part performance requirements. After the latticecells in the same cluster are assigned with the average diameter and thenew design is generated, the new design in the form of a 3D topology issimulated and validated in step 30 to guarantee that all designrequirements are satisfied.

Referring to FIG. 5, after the average diameter of the lattice cells isadjusted, a design with optimal lattice dimension is generated. The newdesign undergoing multiscale topology optimization and lattice dimensionoptimization is shown to have a meshed structure having a mass of 0.744kg, compared to the original design mass of 1.147 kg, resulting in 35.1%mass reduction. The new design also satisfies the design constraint ofstiffness, which is measured by the maximum displacement under loading,whereas the original design was overdesigned, or was heavier thannecessary to meet stiffness requirements.

Referring back to FIG. 1, after the statistical analysis and latticedimension optimization process, the new design is generated in the STLfile (Stereolithography file format) in step 32, which is furthervalidated in step 34. A prototype is then generated in step 36. Steps 32through 36 constitute a CAE-AM integration process. After the CAE fileof the optimal design with the optimized mesh is generated, theoptimized mesh output is smoothed, surfaced, and fixed to satisfyA-Surface requirement.

Referring to FIGS. 6A, 6B, 7A and 7B, two CAE files of two optimaldesigns in the form of optimized mesh output are shown. FIGS. 6A and 6Bshow a first design in the form of tetrahedral mesh. FIGS. 7A and 7Bshow a second design in the form of voxel mesh. In either design, theoptimized mesh output from CAE is coarse. Therefore, the 3D meshtopology is smoothed, surfaced, and fixed to meet A-surfacerequirements. The CAE-AM integration process entails hollowing theA-Surface component and merging in the CAE output through a series ofBoolean operations. The lattice beam output from CAE need to beconverted to surfaces with the appropriate beam and connectionproperties. The optimized structure is further modified to addadditional structural elements, such as geometrical features, requiredby a particular AM process. For example, for selective laser sintering(SLS) or stereolithography (SLA), egress slots may need to be providedin the designed part to allow un-sintered powder or uncured resin toexit the component. For manufacturing methods such as stereolithography(SLA) or direct metal laser sintering (DMLS), the low-angle latticemembers must be filtered out respective to the process constraints toenable no-support lattice manufacture. For fused filament fabrication(FFF) and fused deposition modeling (FDM) methods, the latticestructures must be converted to analogous infill density patternsorthogonal to the build plate surface.

Once the model has been completely prepped and tailored formanufacturing, the model can be brought back into the CAE environment toconfirm the surfacing and geometric edits made do not structurallycompromise the design. Finally, the CAE file is converted to STL file,which is one of the most common file types that 3D printer can read andwhich is used to manufacture the prototype.

The method of designing a part to be formed by an additive manufacturingprocess in accordance with the teachings of the present disclosuresuccessfully integrates computer-aided design (CAD),computer-aided-engineering (CAE), topology optimization (TO), andadditive manufacturing (AM). The method also improves the TO technologyand the lattice structure design method by analyzing the latticedimensions and by distributing solid materials and lattice cells to thelocations under stress/strain. Therefore, the material of a structurecan be re-distributed smartly, thereby reducing material consumption andreducing the weight of parts. When the parts are auto parts, the designwill contribute to enhanced fuel economy. The structure also acceleratesthe process of designing a lightweight structure for AM and generatinginnovative complex structure designs with light weight and highperformance. The method can help eliminate costs for mold creation,economical solution for low volume component design, resulting in aleaner and greener manufacturing.

It should be noted that the disclosure is not limited to the variousforms described and illustrated as examples. A large variety ofmodifications have been described and more are part of the knowledge ofthe person skilled in the art. These and further modifications as wellas any replacement by technical equivalents may be added to thedescription and figures, without leaving the scope of the protection ofthe disclosure and of the present patent.

What is claimed is:
 1. A part formed by an additive manufacturingprocess consisting of three regions: regions of voids; regions of solidmaterial; and regions of non-uniform lattice cells, wherein the regionsare spatially distributed throughout the part as a function of loadconditions such that the solid material is distributed in regions offirst load paths and the lattice cells are distributed in regions ofsecond load paths lower in magnitude than the first load paths.
 2. Thepart according to claim 1, wherein the lattice cells comprise 6-bartetrahedral lattice cells, 16-bar hexahedral elements, and 24-barhexahedral elements, wherein a type and a size of the lattice cells area function of the additive manufacturing process.
 3. The part accordingto claim 2, wherein diameters of each bar of the lattice cells are notequal.
 4. The part according to claim 3, wherein diameters of each barof the lattice are a function of at least one of a resolution unit ofthe additive manufacturing process and part performance requirements. 5.The part according to claim 1, wherein a material of the part isselected from the group consisting of metals, ceramics, polymers,composites and plastics.
 6. A process of designing a part formed by anadditive manufacturing process comprising: generating a 3D mesh topologyconsisting of three (3) regions: regions of voids; regions of solidmaterial; and regions of non-uniform lattice cells, wherein the regionsare spatially distributed throughout the part as a function of loadconditions such that the solid material is distributed in regions offirst load paths and the lattice cells are distributed in regions ofsecond load paths lower in magnitude than the first load paths.
 7. Theprocess according to claim 6, wherein the lattice cells comprise 6-bartetrahedral lattice cells, 16-bar hexahedral elements, and 24-barhexahedral elements, which a type and a size of the lattice cells are afunction of the additive manufacturing process.
 8. The process accordingto claim 7, wherein diameters of each bar of the lattice cells areoptimized based on the load conditions such that the diameters are notequal.
 9. The process according to claim 8, wherein diameters of eachbar of the lattice are adjusted as a function of at least one of aresolution unit of the additive manufacturing process and partperformance requirements.
 10. The process according to claim 9 furthercomprising developing a statistical analysis model to generate astatistical distribution of bar diameters, and further classifying barsof each lattice into clusters, wherein all lattice cells in a samecluster are assigned an average diameter of the cluster.
 11. The processaccording to claim 10, wherein the average diameter of the cluster isadjusted as a function of at least one of a resolution unit of theadditive manufacturing process and part performance requirements. 12.The process according to claim 6 further comprising a validation step toverify that design requirements are met by the 3D mesh topology.
 13. Theprocess according to claim 6 further comprising smoothing, surfacing,and fixing the 3D mesh topology to meet A-surface requirements.
 14. Theprocess according to claim 6 further comprising generating additionalstructural elements as a function of the additive manufacturing process.15. The process according to claim 6 further comprising generatinggeometrical features as a function of the additive manufacturingprocess.
 16. The process according to claim 15, wherein the geometricalfeatures include egress slots for un-sintered powder of a selectivelaser sintering (SLS) process or for un-cured resin of astereolithography (SLA) process.
 17. A method of manufacturing a partusing an additive manufacturing process comprising: manufacturingregions of solid material; manufacturing regions of non-uniform latticecells; and leaving regions of voids with no material; wherein theregions are spatially distributed throughout the part as a function ofload conditions such that the solid material is distributed in regionsof first load paths and the lattice cells are distributed in regions ofsecond load paths lower in magnitude than the first load paths.
 18. Themethod according to claim 17, wherein diameters of each bar of thenon-uniform lattice cells are adjusted as a function of at least one ofa resolution unit of the additive manufacturing process and partperformance requirements.
 19. The method according to claim 17, whereinthe part is manufactured from different additive manufacturingprocesses.
 20. The method according to claim 17, wherein the additivemanufacturing process is selected from the group consisting of selectivelaser sintering (SLS), stereolithography (SLA), fused depositionmodeling (FDM), polyjet (PJ), direct metal laser sintering (DMLS),selective layer melting (SLM), continuous liquid interphase production(CLIP), laminated object manufacturing (LOM).